Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations

• URL: http://arxiv.org/abs/2404.16905v1
• Date: Thu, 25 Apr 2024 11:52:21 GMT
• Title: Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations
• Authors: Shen Zhang, Haojie Zhang, Jing Zhang, Xudong Zhang, Yimeng Zhuang, Jinting Wu,
• Abstract summary: In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. New task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. We propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion.
• Score: 12.095837596104552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. Unraveling the causes of emotions is more challenging. A new task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. In this study, we propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion. In the first stage, Llama-2-based InstructERC is utilized to extract the emotion category of each utterance in a conversation. After emotion recognition, a two-stream attention model is employed to extract the emotion causal pairs given the target emotion for subtask 2 while MuTEC is employed to extract causal span for subtask 1. Our approach achieved first place for both of the two subtasks in the competition.

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